Vincent Segura

The paper identifies genetic regions associated with leaf ecophysiological traits in grapevine under greenhouse and field conditions, showing that multi-environment analysis improves the detection of stable QTLs.

This study uses GWAS to identify genetic loci associated with pigment and lipid metabolism in the microalga Tisochrysis lutea, revealing previously unknown genomic regions linked to these traits.

This paper proposes using singular value decomposition in near infrared spectroscopy pre-processing for phenomic prediction, showing it performs as well as classical chemometrics methods with less parameter optimization.

The paper develops a 4TREE SNP array for three forest tree species to support breeding and conservation programs.

This paper evaluates high-throughput tools for predicting leaf traits in grapevines under different water conditions, finding spectroscopy and poro-fluorometry effective for trait prediction and heritability estimation.

This study investigates drought-induced epigenetic memory in poplar trees, showing persistent molecular changes and distinct adaptive strategies between genotypes.

Multi-environment GWAS of 206 peach and 150 apricot accessions under natural infection finds mostly environment-specific or interactive loci, 60 high confidence QTLs and 87 candidate genes.

This study evaluates phenomic prediction in sorghum breeding, showing it can compete with genomic prediction and is less affected by population structure, though trait-dependent and requiring further optimization.

The paper identifies new developmental stages and metabolic switches in grapevine berries using a metabolomic clock approach based on single-fruit analysis.

The paper uses near-infrared spectroscopy to monitor sugar and acid changes in individual berries, finding that single berries ripen faster than mixed samples.

This paper evaluates phenomic prediction (PP) accuracy in rice breeding, finding that multi-environment data improve PP performance, making it comparable to genomic prediction (GP) when all genotypes have NIRS measurements.

This study performs a genome-wide association analysis to identify genetic loci influencing flowering date in cultivated olive trees using capture sequencing data from 318 genotypes.

This study uses genomic prediction methods to improve grapevine breeding efficiency by identifying superior individuals for specific traits across two breeding programs.

This study applies phenomic prediction to grapevine using spectra to predict 15 traits, finding comparable predictive ability to genomic prediction across traits and populations.

This study evaluates across-population genomic prediction in grapevine for 15 traits, finding predictive abilities of 0.6 for cross means and 0.26 for individual values, suggesting potential for genomic selection in breeding.

The paper integrates genomic and transcriptomic data to predict phenotypes in poplar, finding that eQTLs influence predictability and that beneficial integration reduces predictor redundancy.

The paper assesses whether gene connectivity in co-expression networks improves phenotype prediction, finding that core genes, though more differentiated, do not systematically predict traits better than peripheral genes.

This paper introduces phenomic selection, a low-cost, high-throughput method using near-infrared spectroscopy to predict complex traits in wheat and poplar with accuracy comparable to molecular markers.